来自弥漫星际介质的尘埃偏振辐射的统计描述--一种RWST方法
Statistical description of dust polarized emission from the diffuse interstellar medium -- A RWST approach
摘要：
扩散磁化ISM和银河系前景的统计特征对CMB构成了一个重大挑战。为了解释它们的非高斯统计，我们需要一种数据分析方法，能够有效地量化跨尺度的统计耦合。这些信息被编码在数据中，但当使用传统工具时，如单点统计和功率谱，大部分信息都会丢失。小波散射变换（WST）是数据科学中引入的非高斯过程的低方差统计描述符，为实现这一目标开辟了道路。我们将WST应用于MHD湍流数值模拟计算的尘埃偏振热发射的无噪声地图。我们分析了归一化复斯托克斯映射和偏振分数和偏振角映射。WST产生几千个系数;其中一些系数测量给定尺度下信号的幅度，另一些则表征尺度和方向之间的耦合。方向的依赖性可以与减少WST（RWST），在以前的作品中介绍的角度模型。RWST提供了偏振图的统计描述，量化了它们在各向同性和各向异性贡献方面的多尺度特性。它使我们能够展示地图结构对平均磁场方向的依赖性，并量化数据的非高斯性。我们还使用RWST系数，辅以额外的约束，以生成具有类似统计数据的随机合成地图。它们与原始地图的一致性证明了RWST提供的统计描述的全面性。这项工作是在分析观测数据和建立CMB前景模型方面向前迈出的一步。我们还在https://github.com/bregaldo/pywst上发布了PyWST，这是一个用于执行WST/RWST分析的Python包。
英文摘要：
The statistical characterization of the diffuse magnetized ISM and Galactic foregrounds to the CMB poses a major challenge. To account for their non-Gaussian statistics, we need a data analysis approach capable of efficiently quantifying statistical couplings across scales. This information is encoded in the data, but most of it is lost when using conventional tools, such as one-point statistics and power spectra. The wavelet scattering transform (WST), a low-variance statistical descriptor of non-Gaussian processes introduced in data science, opens a path towards this goal. We applied the WST to noise-free maps of dust polarized thermal emission computed from a numerical simulation of MHD turbulence. We analyzed normalized complex Stokes maps and maps of the polarization fraction and polarization angle. The WST yields a few thousand coefficients; some of them measure the amplitude of the signal at a given scale, and the others characterize the couplings between scales and orientations. The dependence on orientation can be fitted with the reduced WST (RWST), an angular model introduced in previous works. The RWST provides a statistical description of the polarization maps, quantifying their multiscale properties in terms of isotropic and anisotropic contributions. It allowed us to exhibit the dependence of the map structure on the orientation of the mean magnetic field and to quantify the non-Gaussianity of the data. We also used RWST coefficients, complemented by additional constraints, to generate random synthetic maps with similar statistics. Their agreement with the original maps demonstrates the comprehensiveness of the statistical description provided by the RWST. This work is a step forward in the analysis of observational data and the modeling of CMB foregrounds. We also release PyWST, a Python package to perform WST/RWST analyses at: https://github.com/bregaldo/pywst.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
